Exemplar-based methods, also called rehearsal methods, rely on the storage of exemplars from previous tasks. For instance, iCarl [1]}, selects exemplars by their feature space representation and performs classification based on a nearest-class-mean rule in that feature space. EEIL [2]} uses an equal number of exemplars of all classes and a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss in order to learn the new classes.
